Quant Data Platform
Comprehensive data infrastructure for quantitative trading in Chinese A-share market.
Features
1. Real-time Data
- - Live Quotes: Real-time stock prices, volumes
- Tick Data: Level 1 tick-by-tick data
- Order Book: Real-time bid/ask data
- Index Data: Real-time index values
2. Historical Data
- - Daily K-line: OHLCV data since IPO
- Minute Data: 1/5/15/30/60 minute bars
- Tick History: Historical tick data
- Adjustment: Forward/backward adjustment for dividends
3. Alternative Data
- - Sentiment: Social media, forum sentiment
- News: Financial news, announcements
- Fundamentals: Financial statements, ratios
- Insider Trading: Directors' dealings
- Short Interest: Margin trading data
4. Factor Data
- - Technical Factors: 100+ technical indicators
- Fundamental Factors: Financial metrics
- Alternative Factors: Sentiment, attention
- Custom Factors: User-defined factors
5. Data Quality
- - Completeness Check: Missing data detection
- Accuracy Check: Outlier detection
- Timeliness Check: Delay monitoring
- Consistency Check: Cross-source validation
Installation
CODEBLOCK0
Configuration
CODEBLOCK1
Usage
Real-time Data
CODEBLOCK2
Historical Data
CODEBLOCK3
Alternative Data
CODEBLOCK4
Factor Data
CODEBLOCK5
Data Quality
CODEBLOCK6
API Reference
Real-time
| Method | Description |
|---|
| INLINECODE0 | Get real-time quotes |
| INLINECODE1 |
Get tick data |
|
get_order_book(code) | Get order book |
|
subscribe(codes, callback) | Subscribe to updates |
Historical
| Method | Description |
|---|
| INLINECODE4 | Get daily K-line |
| INLINECODE5 |
Get minute data |
|
get_daily_adj(code, adjust) | Get adjusted data |
|
get_trading_dates(start, end) | Get trading dates |
Alternative
| Method | Description |
|---|
| INLINECODE8 | Get sentiment data |
| INLINECODE9 |
Get news |
|
get_fundamentals(code, years) | Get fundamentals |
|
get_short_interest(code) | Get short interest |
Factors
| Method | Description |
|---|
| INLINECODE12 | Get factor values |
| INLINECODE13 |
Calculate custom factors |
|
list_factors() | List available factors |
|
get_factor_metadata(name) | Get factor info |
Quality
| Method | Description |
|---|
| INLINECODE16 | Check data quality |
| INLINECODE17 |
Find missing data |
|
validate(code, date) | Validate data point |
Data Sources
| Type | Source | Update Frequency |
|---|
| Quotes | Tushare, Akshare | Real-time |
| Fundamentals |
Tushare | Daily |
| News | Tushare, Eastmoney | Real-time |
| Sentiment | Custom | Hourly |
| Alternative | Multiple | Varies |
Caching Strategy
CODEBLOCK7
Rate Limiting
| Source | Rate Limit | Strategy |
|---|
| Tushare | 200/min | Token bucket |
| Akshare |
100/min | Token bucket |
| Custom | Unlimited | N/A |
Data Schema
Daily K-line
CODEBLOCK8
Factor Data
CODEBLOCK9
Use Cases
- - Backtesting: Historical data for strategy testing
- Live Trading: Real-time data for execution
- Research: Alternative data for alpha discovery
- Risk Management: Quality monitoring for data integrity
Best Practices
- 1. Cache Aggressively: Reduce API calls
- Monitor Quality: Check data before use
- Handle Missing: Have fallback strategies
- Stay Updated: Sync latest data regularly
Future Capabilities
- - Level 2 data support
- Options/futures data
- Cross-market data (HK, US)
- Real-time streaming API
量化数据平台
面向中国A股市场量化交易的综合数据基础设施。
功能特性
1. 实时数据
- - 实时行情:实时股票价格、成交量
- 逐笔数据:Level 1逐笔成交数据
- 订单簿:实时买卖盘数据
- 指数数据:实时指数值
2. 历史数据
- - 日K线:上市以来的OHLCV数据
- 分钟数据:1/5/15/30/60分钟K线
- 逐笔历史:历史逐笔成交数据
- 复权:前复权/后复权处理
3. 另类数据
- - 舆情:社交媒体、论坛情绪
- 新闻:财经新闻、公告
- 基本面:财务报表、比率
- 内幕交易:董监高交易
- 融券数据:融资融券数据
4. 因子数据
- - 技术因子:100+技术指标
- 基本面因子:财务指标
- 另类因子:情绪、关注度
- 自定义因子:用户自定义因子
5. 数据质量
- - 完整性检查:缺失数据检测
- 准确性检查:异常值检测
- 时效性检查:延迟监控
- 一致性检查:跨源验证
安装
bash
pip install tushare akshare pandas numpy
配置
python
设置Tushare令牌
export TUSHARE
TOKEN=yourtoken_here
或在代码中设置
from quant_data import DataPlatform
platform = DataPlatform(tushare
token=yourtoken)
使用说明
实时数据
python
from quant_data import DataPlatform
platform = DataPlatform()
获取实时行情
quotes = platform.get
realtimequotes([600519, 000858])
print(quotes)
code price change volume amount
600519 1850.00 +12.50 125000 231250000
000858 156.32 +2.18 89000 13912320
获取逐笔数据
ticks = platform.get
tickdata(600519, date=2026-03-22)
获取订单簿
book = platform.get
orderbook(600519)
历史数据
python
获取日K线
daily = platform.get_daily(
codes=[600519, 000858],
start=2020-01-01,
end=2026-03-22
)
获取分钟数据
minute = platform.get_minute(
code=600519,
freq=5min,
start=2026-03-01,
end=2026-03-22
)
获取复权数据
adj = platform.get
dailyadj(code=600519, adjust=qfq)
另类数据
python
获取舆情数据
sentiment = platform.get_sentiment(600519, days=30)
获取新闻
news = platform.get_news(600519, limit=50)
获取基本面数据
fundamentals = platform.get_fundamentals(600519, years=5)
获取融券数据
short = platform.get
shortinterest(600519)
因子数据
python
获取预计算因子
factors = platform.get_factors(
codes=[600519, 000858],
factor
list=[pe, pb, roe, momentum20d, volatility_20d]
)
计算自定义因子
custom = platform.calculate_factors(
code=600519,
factor_config={
name: my_momentum,
formula: close / close.shift(20) - 1,
params: {}
}
)
数据质量
python
检查数据质量
quality = platform.check
quality(600519, daterange=2026-03)
print(quality)
{
completeness: 0.98,
accuracy: 0.99,
timeliness: 0.95,
overall: 0.97
}
查找数据缺口
gaps = platform.find_gaps(600519, start=2026-01-01)
验证数据
valid = platform.validate(600519, date=2026-03-22)
API参考
实时数据
| 方法 | 描述 |
|---|
| getrealtimequotes(codes) | 获取实时行情 |
| gettickdata(code, date) |
获取逐笔数据 |
| get
orderbook(code) | 获取订单簿 |
| subscribe(codes, callback) | 订阅更新 |
历史数据
| 方法 | 描述 |
|---|
| getdaily(codes, start, end) | 获取日K线 |
| getminute(code, freq, start, end) |
获取分钟数据 |
| get
dailyadj(code, adjust) | 获取复权数据 |
| get
tradingdates(start, end) | 获取交易日历 |
另类数据
| 方法 | 描述 |
|---|
| getsentiment(code, days) | 获取舆情数据 |
| getnews(code, limit) |
获取新闻 |
| get_fundamentals(code, years) | 获取基本面数据 |
| get
shortinterest(code) | 获取融券数据 |
因子数据
| 方法 | 描述 |
|---|
| getfactors(codes, factorlist) | 获取因子值 |
| calculate_factors(code, config) |
计算自定义因子 |
| list_factors() | 列出可用因子 |
| get
factormetadata(name) | 获取因子信息 |
质量检查
| 方法 | 描述 |
|---|
| checkquality(code, daterange) | 检查数据质量 |
| find_gaps(code, start) |
查找缺失数据 |
| validate(code, date) | 验证数据点 |
数据源
| 类型 | 来源 | 更新频率 |
|---|
| 行情 | Tushare, Akshare | 实时 |
| 基本面 |
Tushare | 每日 |
| 新闻 | Tushare, 东方财富 | 实时 |
| 舆情 | 自定义 | 每小时 |
| 另类数据 | 多源 | 不定 |
缓存策略
python
配置缓存
platform = DataPlatform(
cache
dir=~/.quantdata/cache,
cache_expire={
daily: 1d,
minute: 1h,
realtime: 0,
fundamentals: 1d
}
)
速率限制
| 数据源 | 速率限制 | 策略 |
|---|
| Tushare | 200次/分钟 | 令牌桶 |
| Akshare |
100次/分钟 | 令牌桶 |
| 自定义 | 无限制 | 不适用 |
数据模式
日K线
code: str # 股票代码
trade_date: date # 交易日期
open: float # 开盘价
high: float # 最高价
low: float # 最低价
close: float # 收盘价
volume: int # 成交量
amount: float # 成交额
turnover: float # 换手率
因子数据
code: str # 股票代码
trade_date: date # 交易日期
factor_name: str # 因子名称
factor_value: float # 因子值
应用场景
- - 回测:策略测试的历史数据
- 实盘交易:执行的实时数据
- 研究:Alpha发现的另类数据
- 风险管理:数据完整性的质量监控
最佳实践
- 1. 积极缓存:减少API调用
- 监控质量:使用前检查数据
- 处理缺失:准备备用策略
- 保持更新:定期同步最新数据
未来功能
- - Level 2数据支持
- 期权/期货数据
- 跨市场数据(港股、美股)
- 实时流式API